Exponential Moving Average + Batch Normalization + fixes in the losses

Hi @amohammadi I'm back to this package. I know we are not supposed to do that, but I was fine tuning so many things and finally I came with a final solution. This MR has 3 different features (despite the branch is called vgg16, there's no vgg16 here).

1 - The Logits estimator has an option to apply an exponential moving averages to the trainable variables and the loss

2 - I introduced the inception_v1 and inception_v2 with batch normalization (I just renamed the inference method that came from the code I copied and pasted). For this I also added some tests to keep track of the number of trainable variables

3 - I added parts of our loss in the tf.add_to_collection LOSSES

Thanks for looking at it. I will look at yours in the afternoon

bob/learn/tensorflow/estimators/Logits.py

@@ -80,6 +80,12 @@ class Logits(estimator.Estimator):
             "scopes": dict({"<SOURCE_SCOPE>/": "<TARGET_SCOPE>/"}),
             "trainable_variables": [<LIST OF VARIABLES OR SCOPES THAT YOU WANT TO RETRAIN>]
         }
+        
+      apply_moving_averages: bool
+        Apply exponential moving average in the training variables and in the loss.
+        https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+        By default the decay for the variable averages is 0.9999 and for the loss is 0.9
+        
     """
 
     def __init__(self,
@@ -269,6 +275,13 @@ class LogitsCenterLoss(estimator.Estimator):
             "scopes": dict({"<SOURCE_SCOPE>/": "<TARGET_SCOPE>/"}),
             "trainable_variables": [<LIST OF VARIABLES OR SCOPES THAT YOU WANT TO TRAIN>]
         }
+        
+      apply_moving_averages: bool
+        Apply exponential moving average in the training variables and in the loss.
+        https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+        By default the decay for the variable averages is 0.9999 and for the loss is 0.9
+        
+      
 
     """